{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Matplotlib数据可视化基础"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.1 基本图形绘制**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想让x和y之间有些数学关系，列表是不太容易进行数学运算的，这时候就可以通过2.1.2小节所讲的Numpy库引入一维数组进行数学运算，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1 = np.array([1, 2, 3])\n",
    "\n",
    "# 第一条线：y = x + 1\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1) # 使用默认参数画图\n",
    "\n",
    "# 第二条线：y = x*2\n",
    "y2 = x1*2\n",
    "# color设置颜色，linewidth设置线宽，单位像素，linestyle默认为实线，“--”表示虚线\n",
    "plt.plot(x1, y2, color='red', linewidth=3, linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [5, 4, 3, 2, 1]\n",
    "plt.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(10)\n",
    "y = np.random.rand(10)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  \n",
    "\n",
    "# 随机生成10000个服从正态分布的数据\n",
    "data = np.random.randn(10000)\n",
    "\n",
    "# 绘制直方图，bins为颗粒度，即直方图的长条形数目，edgecolor为长条形边框颜色\n",
    "plt.hist(data, bins=40, edgecolor='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**补充知识点：在pandas库中的快捷绘图技巧**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001CB625935C0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这种写法只适合pandas中的DataFrame，不能直接用于Numpy的数组\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data)  # 将绘制直方图中的data数组转换成DataFrame()格式\n",
    "df.hist(bins=40, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cb6242b4e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此外，除了写df.hist()外，还可以通过下面这种pandas库里的通用绘图代码绘图：\n",
    "df.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里是通过设置kind参数为hist来绘制直方图，通过这种通用绘图代码，pandas库除了可以便捷的绘制直方图外，它还可以通过设置kind参数快捷地绘制其他图形，演示代码如下，首先通过2.2.1节的知识点创建一个二维DataFrame表格df。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均收入</th>\n",
       "      <th>人均支出</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>8000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>7000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>6500</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    人均收入  人均支出\n",
       "北京  8000  6000\n",
       "上海  7000  5000\n",
       "广州  6500  4000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([[8000, 6000], [7000, 5000], [6500, 4000]], columns=['人均收入', '人均支出'], index=['北京', '上海', '广州'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cb62494a58>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此时可以通过pandas同时绘制折线图或者柱状图，代码如下：\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "df['人均收入'].plot(kind='line')  # kind=line绘制折线图，不设置则默认折线图\n",
    "df['人均收入'].plot(kind='bar')  # kind=bar绘制柱状图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3.2 数据可视化常见小技巧**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 添加文字说明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.title(name)给图画添加标题；通过plt.xlable()，plt.ylable()用于添加x轴和y轴标签。\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('TITLE')  # 添加标题\n",
    "plt.xlabel('X')  # 添加X轴标签\n",
    "plt.ylabel('Y')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 添加图例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过plt.legend()来添加图例，添加前需要设置好lable（标签）参数，代码如下：\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x + 1\n",
    "x1 = np.array([1, 2, 3])\n",
    "y1 = x1 + 1\n",
    "plt.plot(x1, y1, label='y = x + 1') \n",
    "\n",
    "# 第二条线, 设定标签lable为y = x*2\n",
    "y2 = x1*2\n",
    "plt.plot(x1, y2, color='red', linestyle='--', label='y = x*2')\n",
    "\n",
    "plt.legend(loc='upper left') # 图例位置设置为左上角\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 设置双坐标轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的例子可以在一张图里画出两条线，但如果两条线的取值范围相差比较大，那么画出来的图效果便不太好，那么此时如何来画出两条y坐标轴呢？可以在画完第一个图之后，写如下一行代码即可设置双坐标轴。\n",
    "\n",
    "plt.twinx()\n",
    "\n",
    "需要注意的是如果设置了双坐标轴，那么添加图例的时候，每画一次图就得添加一次，而不能在最后统一添加。这里以y = x和y = x^2为例，演示下如何设置双坐标轴，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 第一条线, 设定标签lable为y = x\n",
    "x1 = np.array([10, 20, 30])\n",
    "y1 = x1\n",
    "plt.plot(x1, y1, color='red', linestyle='--', label='y = x')\n",
    "plt.legend(loc='upper left')  # 该图图例设置在左上角\n",
    "\n",
    "plt.twinx()  # 设置双坐标轴\n",
    "\n",
    "# 第二条线, 设定标签lable为y = x^2\n",
    "y2 = x1*x1\n",
    "plt.plot(x1, y2, label='y = x^2') \n",
    "plt.legend(loc='upper right')  # 改图图例设置在右上角\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置图片大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 设置X轴角度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)  # 绘制折线图\n",
    "\n",
    "import pylab as pl\n",
    "pl.xticks(rotation=45)  # 设置角度为45度\n",
    "\n",
    "plt.show()  # 展示图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(6) 中文显示问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [2, 4, 6]\n",
    "plt.plot(x, y)\n",
    "plt.title('中文标题')  # 添加标题\n",
    "plt.xlabel('中文X轴')  # 添加X轴标签\n",
    "plt.ylabel('中文Y轴')  # 添加Y轴标签\n",
    "plt.show()  # 显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(7) 绘制多图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下图所示，有时我们需要在一张画布上输出多个图形，在Matplotlib库中有当前的图形（figure）以及当前轴（axes）概念，其对应的就是当前画布以及当前子图，在一张画布（figure）上可以绘制多个子图（axes）。绘制多图通常采用subplot()函数或subplots()函数，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![图片解释](https://uploader.shimo.im/f/MZH7f7eX4sAg6ZvA.png!thumbnail)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先来讲解subplot()函数，如下图所示，它通常含有三个参数，子图的行数、列数以及第几个子图，例如subplot(221)表示的就是绘制2行2列的子图（共4个子图），并在第1个子图上进行绘图。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![图片解释](https://uploader.shimo.im/f/duzkow47Y5koePsG.png!thumbnail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 演示代码如下：\n",
    "import matplotlib.pyplot as plt\n",
    "# 绘制第一个子图：折线图\n",
    "ax1 = plt.subplot(221)  \n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里plt其实也可以换成ax1\n",
    "\n",
    "# 绘制第二个子图：柱状图\n",
    "ax2 = plt.subplot(222)  \n",
    "plt.bar([1, 2, 3], [2, 4, 6])\n",
    "\n",
    "# 绘制第三个子图：散点图\n",
    "ax3 = plt.subplot(223)  \n",
    "plt.scatter([1, 3, 5], [2, 4, 6])\n",
    "\n",
    "# 绘制第四个子图：直方图\n",
    "ax4 = plt.subplot(224)  \n",
    "plt.hist([2, 2, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了加强大家对画布（figure）和子图（axes）的理解，我们通过下面的代码来做一个简单演示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cb635bbf60>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GkjGPlS7nDkUhiqTVm4vX8PC0ZVz640P48WF7B12ONFBKzdjM2prZ7Dq+1tTMlpvZ5OhHp/SWKLnghXkr+fUzikIUSaeKjVu58al5dGzbkht1pSmrpXqZ+m6grtmEnYHH3H1oekqSXDN58Rp+9cQcin7QWlGIImni7tz09DzWb6pi/MBidm2m9xJns3p/KprZqcBGYHUdS7oCZ5pZqZmNNbOEDd7MhphZmZmVlZeXN7xiySqxKMQO++zBmAHHKwpRJE2enPUZr773Oded1pEj27UKuhzZSUmbsZk1B34DDEuybCbQw92LgWbAGYkW6X2J+Sc+CvHhQcXsuZs2loikw/IvN3HLc+9RckgbBp/UPuhyJA3qu0w9DBjl7uuSbLaZ5+5fR2+XAR3SVZxkL0UhijSO6hrn2klz+J4Z91xQqP0XOaK+y9Q9gKvMbDLQxczGJFgzwcwKowMCegNz01yjZJmV6zbTb2wpoChEkXS7/62PKYtO2Tqg9e5BlyNpkvTM2N27xW5HG/JIM7vN3YfHLbsVeBQw4Dl3f6MxCpXs8GU0CrFyc5WiEEXSbN5n67j39Q/4qaZs5ZyUh364e/fozeG1/nwBkR3Vkucqt1QxYFwkCnHCoBJFIYqk0fZTtvQ+/VyjCVySFluqqhk8voxFqxSFKNIY4qds7bV786DLkTRTM5adFolCfJeZSyv4s6IQRdJusqZs5TxNX5CdEotC/L9Fa7it99GcrShEkbSq2LiVGzRlK+fpzFgaLD4K8cZeh3NxyQ+CLkkkp7g7v356Pus2bdWUrRynM2NpsFgU4uUnt+e/uh8WdDkiOefJWZ/xynurue60wzVlK8epGUuDxKIQLyo+iGG9jgi6HJGcEz9l6zJN2cp5asaywx6Pi0K8TW+xEEk7TdnKP3rNWHbIi/NWcZOiEEUaVWzK1r19CjVlK0/ozFhSNnnxGn75xGxFIYo0ovmfrdeUrTykn6aSkjJFIYo0usiUrdmaspWHdJla6vXeyvUMVBSiSKO74+X3+bh8IxMHacpWvtGZsSS1pHwDAx4spaWiEEUaVfyUrRM7aMpWvlEzljrFohBrHCYMUhSiSGPRlC3RZWpJqHYU4mH7KApRpDFoypaAzowlgfgoxDEDihSFKNKIntKULUHNWGqJj0K8v+9xlLT/ftAlieSs5V9u4vfPvUexpmzlPV2mlm8pClEAzOxKoE/0072AGe5+eYAl5aT4KVsjNWUr76V8Zmxmbc1sdpKvjzWzaWY2PD2lSSbV1DjXP6koRAF3v8/du7t7d2Aq8EDAJeWk2JStW3sfpSlbskOXqe8GEm6nNbPzgCbufgLQ3sw6pKM4yQx353fPvce/5igKUbYxs/2Btu5eFnQtuUZTtqS2lJqxmZ0KbARW17GkOzApevs14MQE9zHEzMrMrKy8vLwBpUpjufu1xUyYvozLuykKUbZzFXBfoi/oeG44TdmSROptxmbWHPgNMCzJshbAiujtCqBt7QXuPtrdi9y9qKCgoCG1SiMYPeVj/v7mx1xUfCDDTlcUokSY2feAU4DJib6u47nh/hidsnX3+YWasiXfSuXMeBgwyt3XJVmzgW2XsFumeL8SsMdLl3P7S4v4aef9uK13J/2GLvFOIrJxy4MuJJdMXryG8dOWMfDHB2vKlmwnlabZA7jKzCYDXcxsTII1s9h2aboQWJqW6qTRxKIQT+5YwL2KQpTv+gkwJegickn8lK2hvXQVSrZX71ub3L1b7Ha0IY80s9vcPX7X9LPAVDNrB5wOdE13oZI+sSjE4w5qzf19FYUo3+Xuvw66hlyiKVtSnx36KRx9u8PCWo0Yd68ksolrOnCKu69PX4mSTvFRiGMvURSiSCZoypbUJ21DP9x9Ldt2VEsILVxZycCHZrLfnrsx/lJFIYpkwqcVm7jl+YWasiVJ6fpknvjki430f3AGLXdpysTBJRTsoShEkcZWXeP86ok5GGjKliSlcZh5YNX6zfQdM0NRiCIZFpuydW+fQk3ZkqR0ZpzjvtzwNX3HzGD95ioevrRYUYgiGaIpW7Ij1Ixz2Fdbqrhk3Ew+W7uZsYpCFMkYTdmSHaXL1DlqS1U1g8aX8f6qSkb3VxSiSCbFpmxNHFSiKVuSEjXjHBQfhfinPl049YjvTCcVkUby1gflmrIlO0yXqXNMfBTiH845mnP0WpVIxqzduJUbnpxLh300ZUt2jM6Mc0h8FOINPzmcvl0VhSiSKe7OTU/PZ+2mrYwbeLymbMkO0ZlxDrnntQ/iohAPDbockbwSP2XrqHbaLCk7Rs04RzwwZQl/e/Ojb6MQtXtTJHM0ZUt2lppxDnhi5nJGvPS+ohBFAqApW5IOes04y700fxU3Pa0oRJGgxKZsjbxAU7ak4XRmnMXe+qCcax6fzbEHtea+vscqClEkwxasiE7Z6rQf5x6jdy5Iw+mnd5aatayCKybM4rBoFOLuzXWRQySTNm+t5prHZ/P9ls0Zca6mbMnOSakZm1kbM+tpZnoHewgsXFnJJeNmsu+eu/KwohBFAhGbsnXP+V00ZUt2Wr3N2MxaAy8AxcCbZlaQYE1TM1tuZpOjH50aoVYhFoVYSstdmjJhULGiEEUCoClbkm6pXNvsDFzr7tOjjflY4NUEax5z96HpLlC22RaF6EwY1FWbRUQCoClb0hjqPTN297eijbgbkbPjaQmWdQXONLNSMxtrZnoBM80qNm6l39hS1m+uYvxARSGKBMHd+fUzkSlbf7qwi6ZsSdqk+pqxAX2AtUBVgiUzgR7uXgw0A85IcB9DzKzMzMrKy8t3ouT889WWKgY8WMqnFZsYO6CITgdouo9IEP757gpeXrCaa3tqypakV0rN2COuAuYBZydYMs/dV0VvlwEdEtzHaHcvcveigoLvvOwsddhSVc3gaBTifX2PVRSiSEA+rdjE7597j+JD2jCkm6ZsSXqlsoFrqJn1j366F7AuwbIJZlZoZk2A3sDcNNaYt6qqa7jqkXcpXVrBPRcUKgpRJCCasiWNLZUz49FAPzObAjQBPjOz22qtuRWYAMwBprn7G+ktM//EohD/vWgNtyoKUSRQsSlbt5xzlDZOSqOod6OVu68Fetb64+G11iwgsqNa0qB2FGI/RSGKBEZTtiQTNIErhGJRiEMUhSgBMrNRZnZW0HUEaUtVNb98Yo6mbEmj01uQQiYWhXjh8Qdyk6IQJSBmdhKwr7s/H3QtQfrjy4v4aM0GJgwq1pQtaVQ6Mw6RSTM/jUQhdtqPEecqClGCYWbNgAeApWZ2TtD1BGXKB+U89J+lDPzxwZzUQe8AkcalZhwSL81fxbCn59GtYwH39lEUogSqP7AQuAsoNrOray/I9bkBazdu5XpN2ZIMUjMOgSlxUYj3KwpRgncMMNrdVwMTgVNqL8jluQGasiVB0E/9gM1aVsHlikKUcPkIiE21KAKWBVhLxmnKlgRBP/kD9P6qSgaOm0nbVrsoClHCZCzwoJldSGS87c8Dridjvp2ydbCmbElmqRkH5JMvNtJvbCm7N2/KxMElikKU0HD3r4Dzg64j06prnGsnRaZs3aMpW5JhasYBiI9CnDi4RBN9RELgH1M+ZubStYy8oJAD2+iYlMzSa8YZ9t0oxD2CLkkk72nKlgRNzTiDvtpSxSXjIlGIYxSFKBIKsSlbbVpoypYER5epM2RLVTWXPVzGwpWV/KPfcXRVFKJIKGjKloSBmnEGVFXX8N+PvsuMTyr4U58u/L8fKgpRJAxiU7Yu+ZGmbEmwdJm6kdXUODc+NY833lcUokiYxE/ZGna6pmxJsHRm3Ijcnd8//x7PzF6hKESREHF3bn42MmVr3MDjNWVLAqcz40Y08vUPeHjaMi476RBFIYqEyNPvruCl+ZqyJeGRUjM2szZm1tPM9m7sgnLFmKlL+Ov/fUSfogP59Rk/1A5NkZD4tGITv9OULQmZepuxmbUGXgCKgTfNLOEuBzMba2bTzGx4mmvMOpNmfsptL77PGZ325fbzFIUoEhaxKVugKVsSLqmcGXcGrnX3EcCrwLG1F5jZeUATdz8BaG9mHdJbZvZ4ORqFeFKHvRWFKBIysSlbt55zlKZsSajU24zd/S13n25m3YicHU9LsKw7MCl6+zXgxNoLcj3/FGDqh+Vc8/gcjjmoNf/odxy7NNWmEJGw0JQtCbNUXzM2oA+wFqhKsKQFsCJ6uwL4zhtpczn/FGDWsrUMeXgW7Qta8OAARSGKhImmbEnYpdSMPeIqYB5wdoIlG4Ddordbpnq/uSIShVhK21a7MGFQCXvurihEkTCJTdm6+/xCTdmSUEplA9dQM+sf/XQvYF2CZbPYdmm6EFialuqywFJFIYqEmqZsSTZI5Qx2NNDPzKYATYDPzOy2Wmueja4ZCVwAvJjeMsNp9fotXDxmBtU1NUwcXKwoRJGQ0ZQtyRb1vrDp7muBnrX+eHitNZVm1j267i53X5+2CkOqYuNW+o6dwfrNVTx6WYmiEEVCJn7K1oOXaMqWhFvadhlFm/akehfmgFgU4vKKTYwfWEznA/YKuiQRqSU2ZWtoryM4en9N2ZJwy6uNVukQi0J8b2Ulo35xLCccqihEkbDRlC3JNmrGOyA+CvGe8wvpcaSiEEXCprrGuW7SXEBTtiR76M2wKYqPQvzDOUfRW0MDRELpH1M+pnRp5BdmTdmSbKEz4xS4O7dEoxCvP60j/U44OOiSRCSB2JStMzrty3nH6hdmyR5qxim49/UPGB+NQrzqlMOCLkdEEthuylZvBbRIdtFl6nqMmbqEvygKUST0YlO2JgwqpnULTdmS7KIz4yRiUYinH60oRJEwm/qhpmxJdlMzrkN8FOKfLlQUouQPM2tqZsvNbHL0o1PQNSWjKVuSC3SZOoFYFGKXA/dSFKLko87AY+4+NOhC6hObslWxcStjB2jKlmQvnRnXEh+FOO6SYkUhSj7qCpxpZqVmNtbMQnsQxKZsXdvzcE3ZkqymZhxn0eptUYgPDypWFKLkq5lAD3cvBpoBZ9ReYGZDzKzMzMrKy8szXiBoypbkFjXjqPgoxAmDSthnj12DLkkkKPPcfVX0dhnQofYCdx/t7kXuXlRQkPkNU5qyJblGzZhIFGLfsTP4pjoShaipPZLnJphZoZk1AXoDc4MuqLbRU5ZQurSCW84+Sser5ITQvhaUKWs3bqXf2Bms26QoRJGoW4FHAQOec/c3Aq5nOwtWrGfk64s1ZUtySl434w1ff8Ml40pZpihEkW+5+wIiO6pDJzZlq/XumrIluSVvm/GWqmouG1/GgpWV/KPvcYpCFMkCsSlbD1+qKVuSW+ptxma2J/A40ATYCPRx96211jQFlkQ/AK529/lprjVtIlGIs5m25Evu7aMoRJFsED9lq1tHTdmS3JLKBq6LgZHufhqwGuiVYE1sSED36EdoG/G2KMTPufWcozj3mAOCLklE6rFuk6ZsSW6rtxm7+yh3fz36aQGwJsGyeocEhOF9ie7OrS8s5JnZK7iuZ0f6KwpRJPTcnZufWUDFxq3c26eLpmxJTkr5rU1mdgLQ2t2nJ/hyvUMCgn5fIsC9b3zIQ/9ZyuATD+G/T1UUokg2eGb2Cl6cv4pf9eyoKVuSs1LawGVmbYC/Aj+rY8k8d/86ejvhkICgjX37E/7y7w+5oOgAbv6pohBFssGnFZv47b8iU7Yu73Zo0OWINJp6z4zNrDnwJHCTuy+rY1mohwRMKvuUP7ywkNOP3pc7zuusRiySBTRlS/JJKmfGg4BjgZvN7GbgTaCZuw+PWxPaIQGvLFjFsH8qClEk28SmbN1zfqGmbEnOq7cZu/t9wH31rAnlkIC3P/yC/3lMUYgi2UZTtiTf5Oxs6neXr2XIhDJFIYpkmS1V1fxKU7Ykz+Rkh1q0upJLHiylYA9FIYpkmztfWcSHmrIleSbnzoxjUYi7NW/CREUhimSVqR+WM+4dTdmS/JNTzXi7KMRBJdr0IZJFNGVL8lnOXKaORSGu3biVRy/rSoe2ikIUyRbxU7bGDjheU7Yk7+TEmXF8FOKYAcdTeKCiEEWyiaZsSb7L+mYcH4U46hfHKgpRJMt8tnYTv9OULclzWd2Mv6mu4erHIlGId5/fWVGIIlmmusa5dtJcHMiGs7sAAAcpSURBVE3ZkvyWta8Zx6IQX1/4ObecrShEkWw0esoSSj/RlC2RrDwzjkUhPj17Bdf27MiAHx0cdEkisoM0ZUtkm6xsxrEoxEEnHsLVikIUyTqasiWyvay7TB2LQjz/uAMYrihEkaykKVsi28uqM+Mno1GIvY7alzvO02/TItlIU7ZEvitrmvErC1YzNBqF+OeLutC0SdaULiJRsSlbh2nKlsh2sqKjRaIQZ1N44F7c31dRiCLZKH7K1p/6dNGULZE4oW/G8VGID11STItdsu5lbhFBU7ZEkgl1M160upKB42ZGohAvVRSiSCaZWVszm52O+9KULZHk6m3GZranmb1sZq+Z2TNmlnDro5mNNbNpZjY8HYUt+zIShbhrs+9FohBbKQpRJMPuBnbb2TvRlC2R+qVyZnwxMNLdTwNWA71qLzCz84Am7n4C0N7MOuxMUZ9XRqIQq6prmKAoRJGMM7NTgY1Ejvmd8sDUyJSt3599lI5lkTrU24zdfZS7vx79tABYk2BZd2BS9PZrwIm1F5jZEDMrM7Oy8vLypN9z9+ZN6LjPHowfWExHRSGKZFT06tdvgGFJ1qR8PO/balfOP+4AfqYpWyJ1Svk1YzM7AWjt7tMTfLkFsCJ6uwL4TmKDu4929yJ3LyooSP7ewj12bcbYSxSFKBKQYcAod19X14IdOZ57H7M//3t+oeYCiCSRUjM2szbAX4FL61iygW2vLbVM9X5FJJR6AFeZ2WSgi5mNCbgekZxX7/uEopesngRucvdldSybReTS9HSgEFictgpFJKPcvVvstplNdvfBQdYjkg9SedPuIOBY4GYzuxl4E2jm7vG7pp8FpppZO+B0oGvaKxWRjHP37kHXIJIP6m3G7n4fcF89ayrNrDvQE7jL3denpzwREZHcl7ZxVu6+lm07qkVERCRF2mglIiISMDVjERGRgKkZi4iIBMzcPfPf1KwcqOttUvH2Br5o5HIag+rOrFyu+wfunnyqRsBSPJ5z+TkKo2ytG7K39p06ngNpxqkyszJ3Lwq6jh2lujNLdYdftv6/qu7My9bad7ZuXaYWEREJmJqxiIhIwMLejEcHXUADqe7MUt3hl63/r6o787K19p2qO9SvGYuIiOSDsJ8Zi4iI5Dw1Y5FGZmZtzKynme0ddC2SOj1vkkmhaMZm1tbMpib5ejMze97M3jGzujKVMy6Fuvc3s8/MbHL0I/D3i5rZnmb2spm9ZmbPRCMyE60ba2bTzGx4oq9nWip1m1lTM1se93h3CqLWWjW1Bl4AioE36/o3ELbHO11S/fcWNqk+b2EV/dk0O+g6UhXGY3dHmdkoMzuroX8/8GYc/Uc/HmiRZNnVwCx3/zHwczPbIyPFJZFi3SXACHfvHv0oz0x1SV0MjHT304DVQK/aC8zsPKCJu58AtDezDhmuMZF66wY6A4/FPd7zM1phYp2Ba919BPAqkTjS7YT08U6XVJ63MKr3eQu5u4Hdgi5iB4Tx2E2ZmZ0E7Ovuzzf0PgJvxkA10AeoTLKmO9sSoaYAYXhDeCp1dwUGm9m7ZnZ7ZspKzt1Hufvr0U8LgDUJlnVn2+P9GnBiBkpLKsW6uwJnmllp9EwzbalkDeXub7n7dDPrRuQsa1qCZd0J2eOdLik+b6GT4vMWSmZ2KrCRyC8/2SJ0x26qzKwZ8ACw1MzOaej9BN6M3b0yhfzjFsCK6O0KoG3jVlW/FOt+mcgP2uOBE8ysc6MXliIzOwFo7e7TE3w5dI93TD11zwR6uHsx0Aw4I6PF1cHMjMgvbmuBqgRLQvt4p0s9z1sopfC8hU70ZYDfAMOCrmUHhfLYTVF/YCFwF1BsZlc35E4Cb8Yp2sC2Sy4tyZ66/+PuX7l7NTAbCMXlRzNrA/wVqOv191A+3inUPc/dV0VvlxGSx9sjrgLmAWcnWBLKxztdUnjeQimF5y2MhgGj3H1d0IXsoFAeuyk6Bhjt7quBicApDbmTbDnoZ7Ht0l0hsDS4UnbIq2a2n5ntDpwGLAi6oOhvzk8CN7l7XcP9Q/d4p1j3BDMrNLMmQG9gbsYKrIOZDTWz/tFP9wIS/ZAM3eOdLik+b6GT4vMWRj2Aq8xsMtDFzMYEXE+qQnfs7oCPgPbR20WkFoL0HaEZ+mFmk929e/T1jiPd/W9xX/sB8BLwBvAjoGv0bDNw9dR9CnAfsJXIb05/q+t+MsXMrgRuZ9s/9jeBZu4+PG5NK2Aq8G/gdCKPd32X5BtVinUfDTwKGPCcu9+c8UJriW70mwTsQuSXsb8DF4X98U6XBM/bfe7+RIAlpSTB83aVh+WHZYpiP5uCriMVYTx2UxXdUPwgkZeXmgE/d/cVyf9WgvvJln9fZtaOyNnDq7nygyrMoj+MegJTopdfpBHp8RbJb1nTjEVERHJVtrxmLCIikrPUjEVERAKmZiwiIhIwNWMREZGAqRmLiIgE7P8DmEKC5xxdNAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (8, 4) # 设置画布大小\n",
    "\n",
    "plt.figure(1)  # 第一张画布\n",
    "ax1 = plt.subplot(121)  # 第一张画布的第一个子图\n",
    "plt.plot([1, 2, 3], [2, 4, 6])  # 这里的plt可以换成ax1\n",
    "\n",
    "ax2 = plt.subplot(122)  # 第一张画布的第二个子图\n",
    "plt.plot([2, 4, 6], [4, 8, 10])\n",
    "\n",
    "plt.figure(2)  # 第二张画布\n",
    "plt.plot([1, 2, 3], [4, 5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用subplot()函数的时候，每次在新的子图上画图时，都得调用subplot()函数，例如第四个子图就得写成ax4 = plt.subplot(224)，那有没有什么办法，一次性就生成多个子图呢？这时候就可以用到subplots()函数，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AO/XavNptSZI6IScIVV17dfEyvnDLFB56+lU+MagvX//gvmzW1akUJEnVYbBS3ZrywgLOvXEyry5ZzpUf68dJA3eudkuSpE7OYKW6k5nc+McX+Ldf/Ik+W23G7ecczHt27FnttiRJMlipvixdvopLxs3g9smzGbpXM/958v5svXm3arclSRJgsFIdef7VJZx9w2Qen7OILx65B194/x5sskmb86tJkrTRGaxUF3772F/54k+mskkEP/z0ezlir+2q3ZIkSX/HYKWatmp18p/3Psk1v3uad++wFT847UB27u1UCpKk2mSwUs1asGQ5X7hlCg8+9QonDdyJSz/8HqdSkCTVNIOVatL0Fxdyzg2Tmff6Mq44cT9OGdS32i1JktQug5Vqzi2PvMC/3vEnmrfclNvOGUK/nbaudkuSJFXEYKWa8eaKVfzrHTMZO/FFDttjW64+5QB693AqBUlS/TBYqSb8Zf4bnH3DJP700iLOf/+7+OKRe9LkVAqSpDpjsFLV3ffEXL54y1RWZ3LtpwZy5L59qt2SJEnrxWClqlm9Ovmv3z3F1b99ir2334ofnDaAXbbpUe22JElabwYrVcXCN5bzxZ9M5f4n5nHigB25/IT96N7NqRQkSfWt4mAVEX2AX2fmAW28fh2wL3BXZl5WUH9qQDNnv8bZN0zir4ve5LIT3sOpB/UlwvOpJEn1b5N1qP0O0L3cCxFxItCUmUOA3SJijyKaU+MZO/EvnDjqYVatTsaeNYTTBu9iqJIkNYyK9lhFxPuBJcCcNkqGAmNLy/cAhwJPbWhzahxvrljFv/3iz9z8yAscvPs2XPOJA9hmi02r3ZYkSYVqN1hFRDfga8BHgHFtlPUAZpeW5wMDyrzPcGA4QN++zqLdmby44A3OvXEy0198jXOG7s6Xh+1Jl6Z12VkqdbyI6A0cCEzJzFeq3Y+k+lTJv24XASMzc+Faahbz9mHCLcq9b2aOzsyBmTmwubl53TtVXfr9k/M4/po/8Ny8JYw+/UAuPGZvQ5VqTkT0Au4EBgH3RYSDlKT1Usm/cEcC50XE/cD+EXFtmZpJtBz+A+gPzCqkO9Wt1auTa377FP/4o0fos+Vm/Pz8Qznq3dtXuy2pLf2AL2Xm5cDdlNnrLkmVaPdQYGYe/tZyKVxdFRGXZeaINcrGAQ9GxA7AscDgohtV/Xht6Qq+9JOp/PbxuXx4/x345on7sXk3Z/ZQ7crMBwAi4nBa9lpdWt2OJNWrdfrXLjOHlhZHtHp+UUQMBYYBV2bma4V0p7rz55cWcc6Nk5i9YCn/9qF386khXvWn+hAtH9STgQXAijKve56opHYVdrJLZi7IzLGZ2daVg2pwP530IieOeog3V6ziJ2cN5h8P3tVQpbqRLc4DpgMfKvO654lKapfHZ7TBlq1cxb/f+WdumPACg3frzTWfGEDzlk6loPoRERcCL2fmj4GtgbVdrCNJbTJYaYO8tHAp5944mal/WchZh+/GBUfv5VV/qkejgbERcSYwk5b5+CRpnRmstN4eevoVzr95CstXrmbUqQM4dr93VLslab1k5gJazhGVpA1isNI6y0xGPfAM37n7CXZv3oIfnH4guzdvUe22JEmqOoOV1smiN1fwlbHTuOfPf+X4fu/gWx/tR49N/RhJkgQGK62Dx+cs4pwbJvOX+W/wr8fvy2cO8ao/SZLWZLBSRe6YOpuLfjqDLTbrws3DB/PeXXtXuyVJkmqOwUprtXzlar7xy8f434dnMWjX3nzvkwew3VabVbstSZJqksFKbZrz2puce+MkJr+wkDMOfScXHbs3XZ1KQZKkNhmsVNb4Z17l/Jsn88byVXzvkwdwfL8dqt2SJEk1z2Clv5GZ/M+Dz/KtXz/BLttszs2fG8wefbasdluSJNUFg5X+z+tvruBfbpvOr2bO4dj3bM+VH+vHlpt1rXZbkiTVjYqCVUT0Bg4EpmTmKx3bkqrhqb++zlk3TOL5V9/gkuP24czD3ulUCpIkraN2z0SOiF7AncAg4L6I+LvbukdEl4h4ISLuLz3264Be1UF+Me0lPvz9h1i0dAU3nnkQnzt8N0OVJEnroZI9Vv2AL2XmhFLIGgDcXabm5sy8sOgG1XFWrFrNN3/5OD986DkO3KUX3//kALbv6VQKkiStr3aDVWY+ABARh9Oy1+rSMmWDgeMj4ghgBnBWZq4sslEVa+6iNznvpsk8OmsBnz54V7563D506+JUCpIkbYhKz7EK4GRgAbCiTMmjwJGZ+XJE/Bg4Dvh5q/cYDgwH6Nu374b0rA30yHPzOe+mySx+cyVXn7I/H95/x2q3JElSQ6hoF0W2OA+YDnyoTMn0zHy5tDwR2KPMe4zOzIGZObC5+e9O09JGkJlc++CzfOJ/JrDFpl0Yd94hhipJkgpUycnrF0bEp0o/bg0sLFM2JiL6R0QTcAIwrcAeVYAly1by+ZuncNldj/EPe2/HHZ8/hL22d34qSZKKVMkeq9HA6RHxe6AJeDEiLmtVcykwBpgKjM/Me4ttUxvi6bmL+fD3H+JXM17mwmP25r9PP5CtnJ9KkqTCVXLy+gJgWKunR7SqmUnLlYGqMb+a8TJfuXUam3Vt4oYzDuLgd21b7ZYkSWpYzrzeoFauWs2Vdz/B6N8/y/47b82o0wbwjp7dq92WJEkNzWDVgOa9vozP3zSZPz43n9MH78KI4/dh0y5N1W5LkqSGZ7BqMJOen8+5N07mtaUruOqk/pw4YKdqtyRJUqdhsGoQmcn1D8/isrseY8de3fnfzwxin3dsVe22JEnqVAxWDeCN5Su56Kcz+Pm0lzhyn+347kn707O7V/1JkrSxGazq3LPzFnPODZN5cu7rXHD0Xpzzvt3ZZBNvoCxJUjUYrOrY3X+aw1fGTqNLU/Djzw7isD2c0V5aHxHRE7iFlrn6lgAnZ+by6nYlqR551906tHLVaq741eOcNWYS72zuwZ1fOMxQJW2YU4GrMvMoYA5wTJX7kVSn3GNVZ15ZvIwv3DyFh595lU8e1Jevf3Bfp1KQNlBmjlzjx2ZgbrV6kVTfDFZ1ZMoLCzj3xsnMX7Kcb3+sHx8fuHO1W5IaSkQMAXpl5oQyrw0HhgP07du34vfc9aK7Cuuvns264gPVbkHaKDwUWAcykzETnuek/x5Pl6bgp+ccbKiSChYRvYFrgM+Wez0zR2fmwMwc2NzsoXdJ5bnHqsYtXb6KS342g9unzOaIvZr5z5MPoOfmTqUgFSkiugG3Ahdn5vPV7kdS/XKPVQ17/tUlfGTkQ/xs6mz++cg9ue4f32uokjrGGcAA4JKIuD8iTq52Q5Lqk3usatS9f/4r/zx2KptE8KNPv5ehe21X7ZakhpWZo4BR1e5DUv2rKFiVzj04EJiSma90bEud26rVyX/85km+d9/TvGfHrRh16oHs3HvzarclSZIq0O6hwIjoBdwJDALui4iyZ21GxHURMT4iRhTcY6cxf8lyPv2jR/jefU9z8sCdue3sgw1VkiTVkUr2WPUDvpSZE0ohawBw95oFEXEi0JSZQyLihxGxR2Y+1QH9Nqxpf1nIuTdOZt7iZVxx4n6cMqjyy7klSVJtaHePVWY+UApVh9Oy12p8mbKhwNjS8j3AoYV12OAyk5v++AIf/0HLr/W2s4cYqiRJqlOVnmMVwMnAAmBFmZIewOzS8nxa9mq1fo/1mlyvkb25YhVfGzeTWye9yOF7NnP1yfvTq0e3arclSZLWU0XTLWSL84DpwIfKlCwGupeWtyj3vk6u97deePUNPjrqYW6d9CJfeP+7+NGn32uokiSpzrW7xyoiLgRezswfA1sDC8uUTaLl8N8EoD/wRJFNNpr7Hp/LF38ylczkun8cyD/s06faLUmSpAJUcihwNDA2Is4EZgIvRsRlmbnm1X/jgAcjYgfgWGBw8a3Wv1Wrk6t/+xTX/O4p9t5+K/77tAPpu41X/UmS1CjaDVaZuQAY1urpEa1qFkXE0FLdlZn5WmEdNoiFbyznn26ZygNPzuOjA3bishPeQ/duTdVuS5IkFaiwmddLAWxsu4Wd0MzZr3H2DZOYu2gZl3/kPXxyUF9argeQJEmNxFvadLCxj/6FEXfMZNse3Rh79hD233nrarckSZI6iMGqg7y5YhX/7+d/4pZH/8Kh79qWq0/Zn2222LTabUmSpA5ksOoALy54g3NumMyM2a9x3hG786Vhe9G0iYf+JElqdAargj3w5Dz+6ZYprFqVjD79QI569/bVbkmSJG0kBquCrF6dfO++p/mPe59krz5bMuq0A3nntj2q3ZYkSdqIDFYFeO2NFfzz2Kn87vG5fOSAHbn8I+9h827+aiVJ6mz8138D/eml1zjnhsm8/NpSLv3wuzl98C5OpSBJUidlsNoAt016kUt+NoNem3fjluFDOHCXXtVuSZIkVZHBaj0sW7mKS3/xZ2784wsM2W0brvnkAWzrVAqSJHV6Bqt19NLCpZxz42Sm/WUhZ71vNy44ai+6NG1S7bYkSVINMFitgz889QpfuGUKy1eu5genDeCY97yj2i1JkqQaYrCqwOrVyagHnuG79zzBu7bbglGnHcjuzVtUuy1JklRjDFbteG3pCr48dhr3PvZXPth/B644cT96bOqvTZIk/b12E0JE9ARuAZqAJcDJmbm8VU0X4NnSA+D8zJxRcK8b3eNzFnH2mEm8uGApX//gvnz64F2dSkFqYBHRB7gtMw+rdi+S6lMlZ12fClyVmUcBc4BjytT0A27OzKGlR92HqnFTZnPC9x/ijeWruGX4YD5zyDsNVVIDi4hewPWAt0yQtN7a3WOVmSPX+LEZmFumbDBwfEQcAcwAzsrMlcW0uHEtX7may+/6M9ePf55B7+zN9z55ANttuVm125LU8VYBJwN3VLsRSfWr4nkCImII0CszJ5R5+VHgyMwcBHQFjivz54dHxMSImDhv3rz1brgjvfzaUk4ZPZ7rxz/P5w57JzeeeZChSuokMnNRZr7W1uv1MIZJqr6KzsKOiN7ANcBH2yiZnpnLSssTgT1aF2TmaGA0wMCBA3PdW+1YDz/zCl+4eQpLl6/i+58cwAf6OZWCpLfV+hgmqTa0u8cqIroBtwIXZ+bzbZSNiYj+EdEEnABMK7DHDpWZ/OCBZzjt2j/Ss3tX7vj8IYYqSZK0XirZY3UGMAC4JCIuAe4DumbmiDVqLgVuAgL4eWbeW3inHeD1N1dwwa3T+fWf5nDcfttz5cf6s4VTKUiSpPVUycnro4BR7dTMpOXKwLrx5F9f5+wxk3h+/huM+MA+nHGoV/1JgswcWu0eJNWvTrl75ufTXuLC26bTY9Mu3HTmQRy02zbVbkmSJDWAThWsVqxazTd++Rg/emgWA3fpxfdPHUCfrbzqT5IkFaPTBKu5i97k3BsnM/H5BXzmkF356nH70LWp4tkmJEmS2tUpgtUfn32V826awpJlK7n6lP358P47VrslSZLUgBo6WGUm1/3hOb75q8fZpffm3PS5g9izz5bVbkuSJDWohg1Wi5et5MLbpnPXjJc5+t19+M7H+7PlZl2r3ZYkSWpgDRmsnp77OmeNmcRzryzh4mP3ZvjhuzmVgiRJ6nANF6x+OeNlLrh1Gpt1beKGMw7i4HdtW+2WJElSJ9EwwWrlqtV869eP8z8PPscBfbdm5KkDeEfP7tVuS5IkdSINEazmvv4mn79pCo88N59PDdmFER/Yl25dnEpBkiRtXHUfrCbOms+5N05m0Zsr+I+T+/ORA3aqdkuSJKmTqttglZn86KFZfOOXj7FTr+5c/9lB7POOrardliRJ6sTqMlgtWbaSi26fwS+mvcSR+/Thuyf1p2d3p1KQJEnVVXfB6tl5izn7hkk8PXcxFxy9F+e8b3c22cSpFCRJUvXVVbD69cw5fOXWaXTrsgk//uxBHLqHUylIkqTa0W6wioiewC1AE7AEODkzl5epuw7YF7grMy8rssmVq1bznXue5AcPPEP/nXoy8rQD2XFrp1KQJEm1pZI5CU4FrsrMo4A5wDGtCyLiRKApM4cAu0XEHkU1+MriZZx+3SP84IFnOPWgvow9e4ihSpIk1aR291hl5sg1fmwG5pYpGwqMLS3fAxwKPLWhza1enZx27R957pUlfKmRhi0AABxHSURBVOfj/fnYgU6lIEmSalfF51hFxBCgV2ZOKPNyD2B2aXk+MKDMnx8ODAfo27dvRX/nJpsEXzt+X7bevCvv3qFnpa1KkiRVRUXBKiJ6A9cAH22jZDHw1vG5LShziDEzRwOjAQYOHJiVNniI9/qTJEl1ot1zrCKiG3ArcHFmPt9G2SRaDv8B9AdmFdKdJElSHalkj9UZtBzauyQiLgHuA7pm5og1asYBD0bEDsCxwODCO5UkSapxlZy8PgoY1U7NoogYCgwDrszM14ppT5IkqX4UNkFoZi7g7SsDJUmSOp1K5rGSpIYXEddFxPiIGNF+tSSVZ7CS1Ol15CTHkjoXg5UklZ/kWJLWWWRWPKVUcX9pxDygrakbytkWeKWD2qklnWE9O8M6gutZzi6Z2dyRzayv0r1O/yszp0XEUcCAzLyiVc3/TXIM7AU8UVquh21tj8Wphz7tsRhr9ljx+FXYyevrYl0H14iYmJkDO6qfWtEZ1rMzrCO4nnVonSY5XlM9/A7ssTj10Kc9FmN9e/RQoCQ5ybGkglRlj5Uk1RgnOZZUiHrZY/V3u98bVGdYz86wjuB61pXMXETLCewTgCPWcZLjevgd2GNx6qFPeyzGevVYlZPXJUmSGlG97LGSVCUR0TsihkXEttXuRVJ9qYfxo+geaypYRUSfiHhwLa93jYhfRMRDEfHZjdlbkSpYzx0j4sWIuL/0qMlL1NsSET0j4lcRcU9E/CwiurVRV9czXVeynhHRJSJeWGNb7leNXtdXRPQC7gQGAfe19Vms921ZifbWsVa2dT2Mo7U8BtbL+FUP4089jB+V9Liuv8eaCVallbse6LGWsvOBSZl5CPCxiNhyozRXoArX8yDg8swcWnrM2zjdFeZU4KrMPAqYAxzTuqBBZrpudz2BfsDNa2zLGRu1ww3XD/hSZl4O3A0MaF3QINtyrSpcx6pv63oYR+tgDKyX8asexp96GD/a7ZF1/D3WTLACVgEnA4vWUjOUt2dH/j1Q03NgtKGS9RwMnBkRkyPiGxunreJk5sjM/E3px2ZgbpmyodT5TNcVrudg4PiIeKT0rayursTNzAcyc0JEHE7LN7rxZcqGUufbsgJDaX8da2Fb18M4WtNjYL2MX/Uw/tTD+FFhj+v0e6yZYJWZiyq4EqcHMLu0PB/o07FdFa/C9fwVLR+29wJDIqJfhzfWASJiCNArMyeUebnut+Vb2lnPR4EjM3MQ0BU4bqM2V4CICFr+IVwArChT0jDbci0qWceqb+t6GEfrZQysl/Gr1sefehg/KuhxnX6PNROsKtTu7MgN4uHMfD0zVwFTgLo7tBIRvYFrgLbO4WiIbVnBek7PzJdLyxOpw22ZLc4DpgMfKlPSENuyHZWsY71s63rYXlUdA+tl/KqH8acexo8Kelyn32Mt/g+1Np1lduS7I+IdEbE5cBQws9oNrYvSSZS3AhdnZlv3hKz7bVnheo6JiP4R0QScAEzbaA0WICIujIhPlX7cGlhYpqzut2UFKlnHetnW9bC9qjYG1sv4VQ/jTz2MHxX2uG6/x8ysqQdwf+m/7wc+3+q1XYA/AVfTsmuuqdr9dtB6HgE8Tkt6/vzG7q2AdTuHll2q95ceXwcua1WzVenDeRXwGNCz2n130Hq+p7QdZ9ByMm7V+17HdewF/IaWc3FGAu9uxG1Zwe+h9Tr2r+VtXQ/jaK2OgfUyftXD+FMP40eFPa7T77HuJgiNlltOHArcnes2O7JqTOnqoGHA7zNzTrX70frrDNuykdbRcXTDNdLnodoa7XdZd8FKkiSpVtXbOVaSJEk1y2AlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBulTjL912221z1113rcZfLalKJk2a9EpmNle7jyI4hkmdy7qMX1UJVrvuuisTJ06sxl8tqUoi4vlq91AUxzCpc1mX8ctDgZIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSGlpE9I6IYRGxbbV7kdT4Kg5WETEyIj64ltevi4jxETGimNYk1aJxU2ZzyBW/450X3cUhV/yOcVNmV7ulNkVEL+BOYBBwX0SUvVza8UtSUSoKVhFxGLB9Zv6ijddPBJoycwiwW0TsUWCPkmrEuCmzufj2GcxeuJQEZi9cysW3z6jlcNUP+FJmXg7cDQxoXeD4JalI7QariOgK/A8wKyI+3EbZUGBsafke4NBCupNUU7599xMsXbHqb55bumIV3777iSp1tHaZ+UBmToiIw2nZazW+TNlQHL8kFaSSCUI/BfwZuBI4PyL6ZuY1rWp6AG99ZZ1P+W+Fw4HhAH379l3vhiVVz0sLl67T87UgIgI4GVgArChT0u74VXqf9RrDdr3ornXodt3MuuIDHfbektZPJYcCDwBGZ+Yc4AbgiDI1i4HupeUtyr1vZo7OzIGZObC5uSHuaiF1Ojts3X2dnq8F2eI8YDrwoTIl7Y5fpfdxDJPUrkqC1dPAbqXlgUC5ad0n8fbu8/7ArA3uTFLNueDovejetelvnuvetYkLjt6rSh2tXURcGBGfKv24NbCwTJnjl6TCVHIo8DrghxFxCtAV+EpEXJaZa149Mw54MCJ2AI4FBhffqqRqO+GAHYGWc61eWriUHbbuzgVH7/V/z9eg0cDYiDgTmAm86PglqSO1G6wy83Xg462efqhVzaKIGAoMA67MzNcK61BSTTnhgB1rOUj9jcxcQMu4tKYRrWocvyQVppI9VhUpDWBj2y2UpBrj+CWpKM68LkmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVJC1BquI6BIRL0TE/aXHfm3UTV2jZljHtCpJklTburTzej/g5sy8sK2CiNgGeDwzTym0M0mSpDrT3qHAwcDxEfFIRFwXEeWC2EHAoIh4OCLGRcSWxbcpSZJU+9oLVo8CR2bmIKArcFyZmmeBozPzYGA68JlybxQRwyNiYkRMnDdv3ob0LEmSVJPaC1bTM/Pl0vJEYI8yNc8CT7dTQ2aOzsyBmTmwubl5vZqVJEmqZe0FqzER0T8imoATgGllai4HPlha/lgbNZIkSQ2vvWB1KTAGmAqMByZHxLWtaq4CLomImcAy4PrCu5QkSaoDa70qMDNn0nJl4JrObFXzMi0nsEuSJHVqThAqSZJUkPbmsZKkuhURPYFbgCZgCXByZi5vVdOFlotwni09dX5mztiojUpqGO6xktTITgWuysyjgDnAMWVq3poIeWjpYaiStN7cYyWpYWXmyDV+bAbmlil7ayLkI4AZwFmZuXJj9Cep8bjHSlLDi4ghQK/MnFDm5UomQnaSY0kVMVhJamgR0Ru4BvhsGyWVTITsJMeSKmKwktSwIqIbcCtwcWY+30ZZJRMhS1JFPMdKUiM7AxhAyyTGlwD3AV0zc8QaNZcCNwEB/Dwz7934bUpqFAYrSQ0rM0cBo9qpKTcRsiStFw8FSpIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklSQdoNVRHSJiBci4v7SY7826v4tIh6NiO8X36YkSVLtq2SPVbt3fo+IA4FDgUHA3Ig4suA+JUmSal4lweqtO78/EhHXRUS5SUXfB/w0MxO4GzisdYE3MJUkSY2ukmBVyZ3fewCzS8vzgT6tC7yBqSRJanSV3NJmemYuKy23def3xUD30vIWeFK8JEnqhCoJQJXc+X0SLedYAfQHZhXTniRJUv2oJFhdCowBpgLjgckRcW2rmj8AB0TE1cBFwM2FdilJklQH2j0U2Mad389sVbO6dCXgB4CrM/O54lqUJEmqD5WcY1WRzFwK3FbU+0mSJNUbTzKXJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSqIwUpSw4qInhHxq4i4JyJ+FhHd2qi7LiLGR8SIjd2jpMZisJLUyE4FrsrMo4A5wDGtCyLiRKApM4cAu0XEHhu5R0kNpLB7BUpSrcnMkWv82AzMLVM2FBhbWr4HOBR4qnVRRAwHhgP07du30D4lNY6K9lhFRJ+ImNLGa10i4oWIuL/02K/YFiVpw0TEEKBXZk4o83IPYHZpeT7Qp9x7ZObozByYmQObm5s7qFNJ9a7SPVbfAbq38Vo/4ObMvLCYliSpOBHRG7gG+GgbJYt5e3zbAk+RkLQB2h1AIuL9wBJazk8oZzBwfEQ8UjoB1MOLkmpC6WT1W4GLM/P5Nsom0XL4D6A/MGsjtCapQa01WJUGpa8BF62l7FHgyMwcBHQFjiuuPUnaIGcAA4BLSqcqfD0iLmtVMw44PSKuAk4C7trYTUpqHO3tXboIGJmZCyOirZrpmbmstDwRKHtFjSd+StrYMnMUMKqdmkURMRQYBlyZma9tjN4kNab2DgUeCZwXEfcD+0fEtWVqxkRE/4hoAk4AppV7I0/8lFSrMnNBZo7NzLZOeZCkiqx1j1VmHv7WcilcXRURl2XmmpPoXQrcBATw88y8tyMalSRJqnUVn2iemUNLiyNaPT+TlisDJUmSOjUvK5YkSSqIwUqSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSqIwUqSJKkgBitJkqSCdKm0MCL6AL/OzAPaeP06YF/grsy8rKD+1CDGTZnNt+9+gpcWLmWHrbtzwdF7ccIBO1a7LUmSCrUue6y+A3Qv90JEnAg0ZeYQYLeI2KOI5tQYxk2ZzcW3z2D2wqUkMHvhUi6+fQbjpsyudmuSJBWqomAVEe8HlgBz2igZCowtLd8DHLrBnalhfPvuJ1i6YtXfPLd0xSq+ffcTVepIkqSO0W6wiohuwNeAi9ZS1gN4a/fDfKBPmfcZHhETI2LivHnz1qdX1amXFi5dp+clSapXleyxuggYmZkL11KzmLcPE25R7n0zc3RmDszMgc3NzeveqerWDluXPYLc5vNS0SKiT0Q8uJbXd4yIFyPi/tLDQUrSeqkkWB0JnBcR9wP7R8S1ZWom8fbhv/7ArEK6U0O44Oi96N616W+e6961iQuO3qtKHakziYhewPW07Flvy0HA5Zk5tPRwt7qk9dLuVYGZefhby6VwdVVEXJaZI9YoGwc8GBE7AMcCg4tuVPXrrav/vCpQVbIKOBm4Yy01g4F/iIjP0XL181c3SmeSGk7F0y0AZObQ0uKIVs8vioihwDDgysx8rZDu1DBOOGBHg5SqIjMXAUTE2sp+Bfw78AZwb0T0y8zpG6E9SQ2msAlCM3NBZo7NzLauHJSkWvVwZr6emauAKcDfTRnjBTiSKuHM65IEd0fEOyJic+AoYGbrAi/AkVSJdToUKEn1rjQv376Z+b01nv434D5gOfCDzHSSNUnrxWAlqVN46xzRzPwd8LtWr90H7F2FtiQ1GA8FSpIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVpKJgFRG9I2JYRGzb0Q1JkiTVq3aDVUT0Au4EBgH3RcTf3X00IrpExAsRcX/psV8H9CpJklTTKrlXYD/gS5k5oRSyBgB3l6m5OTMvLLpBSZKketHuHqvMfKAUqg6nZa/V+DJlg4HjI+KRiLguIry5syRJ6nQqPccqgJOBBcCKMiWPAkdm5iCgK3BcmfcYHhETI2LivHnzNqBlSZKk2lRRsMoW5wHTgQ+VKZmemS+XlicCe5R5j9GZOTAzBzY3/91pWpIkSXWvkpPXL4yIT5V+3BpYWKZsTET0j4gm4ARgWoE9SpIk1YVK9liNBk6PiN8DTcCLEXFZq5pLgTHAVGB8Zt5bbJuSJEm1r92TzDNzATCs1dMjWtXMpOXKQEmSpE7LmdclSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSlLDi4g+EfHgWl7vGhG/iIiHIuKzG7M3SY3FYCWpoUVEL+B6oMdays4HJmXmIcDHImLLjdKcpIZjsJLU6FbRchP5RWupGQqMLS3/HhjYwT1JalDtzrwuSfUsMxcBRMTaynoAs0vL84E+rQsiYjgwHKBv377FNqm6setFd3XYe8+64gMd9t71qh5/3+6xkiRYDHQvLW9BmbExM0dn5sDMHNjc3LxRm5NUPwxWkgSTgENLy/2BWdVrRVI981CgpE4lIt4P7JuZ31vj6euBX0bEYcC+wB+r0pykuuceK0mdQmYOLf33d61CFZn5PDAMeAg4MjNXbfwOJTWCioJVRPSOiGERsW1HNyRJ1ZCZL2Xm2Mx8rdq9SKpf7Qar0hwwdwKDgPsiouxZmxFxXUSMj4gRBfcoSZJUFyrZY9UP+FJmXg7cDQxoXRARJwJNmTkE2C0i9ii2TUmSpNrXbrDKzAcyc0JEHE7LXqvxZcqG8vbkevfw9tU1kiRJnUal51gFLTMXLwBWlCmpaHK9iJgYERPnzZu3nu1KkiTVroqCVbY4D5gOfKhMiZPrSZKkTq+Sk9cvjIhPlX7cGlhYpszJ9SRJUqdXyR6r0cDpEfF7oAl4MSIua1UzrlRzFXAS0HE395EkSapR7c68npkLaJk4b00jWtUsioihpbornQdGkiR1RoXd0qYUwMa2WyhJktSgvKWNJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQbq0VxARPYFbgCZgCXByZi5vVdMFeLb0ADg/M2cU3KskSVJNq2SP1anAVZl5FDAHOKZMTT/g5swcWnoYqiTVhIi4LiLGR8SINl7vEhEvRMT9pcd+G7tHSY2j3WCVmSMz8zelH5uBuWXKBgPHR8QjpUGs3T1hktTRIuJEoCkzhwC7RcQeZcr8YiipMBWfYxURQ4BemTmhzMuPAkdm5iCgK3BcmT8/PCImRsTEefPmrXfDkrQOhgJjS8v3AIeWqanoi6FjmKRKVBSsIqI3cA3w2TZKpmfmy6XlicDffSvMzNGZOTAzBzY3N69Xs5K0jnoAs0vL84E+ZWra/WIIjmGSKtNusIqIbsCtwMWZ+XwbZWMion9ENAEnANMK7FGS1tdioHtpeQvKj3ntfjGUpEpVssfqDGAAcEnpxM6vR8RlrWouBcYAU4HxmXlvwX1K0vqYxNuH//oDs8rU+MVQUmHaPck8M0cBo9qpmUnLCaCSVEvGAQ9GxA7AscApEXFZZq55heClwE1AAD/3i6GkDeHVe5IaVmYuioihwDDgysycQ6s9Un4xlFQkg5WkhpaZC3j7ykBJ6lDe0kaSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSpIuzdhjoiewC1AE7AEODkzl5epuw7YF7grMy8rqsFxU2bz7buf4KWFS9lh6+5ccPRenHDAjkW9vSRJUmEq2WN1KnBVZh4FzAGOaV0QEScCTZk5BNgtIvYoorlxU2Zz8e0zmL1wKQnMXriUi2+fwbgps4t4e0mSpEK1G6wyc2Rm/qb0YzMwt0zZUGBsafke4NAimvv23U+wdMWqv3lu6YpVfPvuJ4p4e0mSpEJVfI5VRAwBemXmhDIv9wDe2o00H+hT5s8Pj4iJETFx3rx5Ff2dLy1cuk7PS5IkVVNFwSoiegPXAJ9to2Qx0L20vEW5983M0Zk5MDMHNjc3V9TcDlt3X6fnJUmSqqndYBUR3YBbgYsz8/k2yibx9uG//sCsIpq74Oi96N616W+e6961iQuO3quIt5ckSSpUu1cFAmcAA4BLIuIS4D6ga2aOWKNmHPBgROwAHAsMLqK5t67+86pASZJUD9oNVpk5ChjVTs2iiBgKDAOuzMzXimmvJVwZpCRJUj2oZI9VRTJzAW9fGShJktTpOPO6JElSQQxWkhpaRFwXEeMjYsSG1EhSJQxWkhpWJXeF6Kg7R0jqnAxWkhrZUNq/K0QlNZJUkcJOXl8XkyZNeiUi2poTqy3bAq90RD81xHVsHJ1hPdd1HXfpqEbWovVdIQasZw0RMRwYXvpxcURUem+tDvssxLc64l3XW2f4zEPn2J6dYlvGt9ZpPSsev6oSrDKzsqnX1xAREzNzYEf0Uytcx8bRGdazTtax3btCVFhDZo4GRq9rA3Xye9pgrmfj6AzrCB23nh4KlNTIKrkrRIfcOUJS51SVPVaStJG0vivEKRFx2ca4c4Skzqme9lit8y74OuQ6No7OsJ41v46ZuYiWk9MnAEdk5rRWoapcTWF3jiip+d9TQVzPxtEZ1hE6aD0jMzvifSVJkjqdetpjpToXEb0jYlhEbFvtXiSps3NM7hh1Eawiok9EPFjtPjpKRPSMiF9FxD0R8bOI6FbtnooWEb2AO4FBwH0Rsc5XhtaL0ud1SrX76CgR0SUiXoiI+0uP/ardkzaOSsaqRvl8NHroqGRMbpRtubHVfLAqbfzraZlrplGdClyVmUcBc4BjqtxPR+gHfCkzLwfupo25ghrEd3j78v1G1A+4OTOHlh4zqt1QtVX65agBbp1TyVhV95+PSr8I1vn2rGRMrvtt+Zb2vvAWuS1rPlgBq4CTgUXVbqSjZObIzPxN6cdmYG41++kImflAZk6IiMNpGazGV7unjhAR7weW0PKPTqMaDBwfEY+UBiOvLq4gcDTCrXMqHKsa4fPRbuio9+1Z4ZjcCNvyLW1+4S16W9Z8sMrMRR1wlU5NioghQK/MnFDtXjpCRAQtIXkBsKLK7RSutJfia8BF1e6lgz0KHJmZg4CuwHFV7qfqKgwcQ2mQW+e0M1bV/eejwtAxlDrfnhWMyXW/LaGiL7xDKXBb1nyw6iwiojdwDfDZavfSUbLFecB04EPV7qcDXASMzMyF1W6kg03PzJdLyxOBuvqm3pHaCRytb53TZ6M1VqAKxqqG+HxUEDrqfntWMCbX/bas8AtvodvSYFUDShv+VuDizFzXeyjWhYi4MCI+Vfpxa6ARw8eRwHkRcT+wf0RcW+V+OsqYiOgfEU3ACcC0ajdUCyoIHBXdOqeWVThWNcTno4LQUdfbs8IxuRG2ZSVfeAvdlnX1QWhgZ9ByDP+S0pUXJ1e7oQ4wGjg9In4PNNGyu7WhZObhb53kCUzNzDOr3VMHuRQYA0wFxmfmvVXup+oqDByNcOuc1mPV1yPislY1df/5qDB01Pv2bD0mv9iI25LKvvAWui2dIFSSNlBEnAN8g7e/0d8HdF1zlveI2Ap4EPgtpVvndJbzR+tN6arAscCmwEzg+8An3J71rRSuzgU+2ZHb0mAlSRtJ6R/sYcDvM7ORrxztFNyejaPIbWmwkiRJKojnWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQV5P8DUu02U+e3GcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此外，如果要在subplot()函数或者subplots()函数生成的子图中设置子图标题、X轴标签或Y轴标签，得通过set_title()函数、set_xlabel()函数、set_ylabel()函数进行设置，演示代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 0., 0., 0., 1., 0., 0., 0., 1.]),\n",
       " array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8)) \n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "ax1.plot([1, 2, 3], [2, 4, 6])  # 绘制第一个子图\n",
    "ax1.set_title('子图1')\n",
    "ax1.set_xlabel('日期')\n",
    "ax1.set_ylabel('分数')\n",
    "ax2.bar([1, 2, 3], [2, 4, 6])  # 绘制第二个子图\n",
    "ax3.scatter([1, 3, 5], [2, 4, 6])  # 绘制第三个子图\n",
    "ax4.hist([2, 2, 2, 3, 4])  # 绘制第四个子图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
